An investigation on Energy-Efficient Clustering Algorithm for Distributed Ad-hoc based Wireless Sensor Networks


Dr. Sreejith Vignesh B P, Assistant Professor & Head
Department of Information Technology, Sri Krishna Adithya College of Arts and Science, Coimbatore.
Dr. K. Munusamy, Assistant Professor & Head
Department of Computer Science Excel College of Commerce and Science, Komarapalayam.


Wireless sensor networks (WSNs) are made up of a large number of Micro Electro Mechanical Systems (MEMS) with limited capabilities (power and computation) that can measure and report physical variables in their surroundings. Sensors are placed in a specific field in surveillance applications to detect and report events such as presence, movement, or trespass in the monitored area. In the design of sensor network applications and protocols, minimizing energy dissipation and optimizing network lifespan are critical considerations. Finding an appropriate assignment of states to sensors in order to optimise network lifespan is the goal of energy-efficient sensor state planning. In large-scale cluster-based monitoring wireless sensor networks, the present approach established a centralized mechanism for near-optimal state assignment to sensors. The previous one used a tabu method to calculate a near-optimal network configuration in which each sensor may be turned on, turned off, or promoted to cluster leader. The current approach increases network lifespan while guaranteeing that the monitored region is fully covered and that the resulting configuration is connected. An efficiently calculated spanning tree connects all of the cluster heads, providing connectivity. The present tabu-based optimal energy setup has grown complicated due to anomalous node distribution in the case of land surveillance. Furthermore, the tabu method ensures that the probabilistic event detection for each node is independent. In order to get rid of the aberrant node, This paper proposes a distributed energy efficient method for detecting distribution events. This thesis’ suggested work is to provide a more complex heuristic to increase network lifespan. The suggested approach deals with distance-dependent probabilities. The probability depending on distance is a function of the distance between the sensor and the event. Under the joint coverage and routing constraints, the suggested system develops a distributed method that solves energy-efficient clustering. The suggested model is tested using Network Simulator 2 (NS-2) for different simulation times, routing topologies, and other parameters.